system

Abstract

The system according to this embodiment aims to streamline event management, accounting management, and the transmission of emergency information. [Solution] The system according to the embodiment comprises a date and time adjustment unit, a reminder setting unit, an accounting management unit, a document distribution unit, an information collection unit, an information distribution unit, and a group chat unit. The date and time adjustment unit adjusts the date and time. The reminder setting unit notifies the date and time adjusted by the date and time adjustment unit. The accounting management unit performs accounting management. The document distribution unit distributes accounting information managed by the accounting management unit. The information collection unit collects disaster information. The information distribution unit notifies the disaster information collected by the information collection unit. The group chat unit provides chat tailored to specific needs.

Description

Technical Field

[0001] The technology of the present disclosure relates to a system.

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that event management, accounting management, and transmission of emergency information are complicated and efficient operation is difficult.

[0005] The system according to the embodiment aims to improve the efficiency of event management, accounting management, and transmission of emergency information.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a date and time adjustment unit, a reminder setting unit, an accounting management unit, a document distribution unit, an information collection unit, an information distribution unit, and a group chat unit. The date and time adjustment unit adjusts the date and time. The reminder setting unit notifies the date and time adjusted by the date and time adjustment unit. The accounting management unit performs accounting management. The document distribution unit distributes accounting information managed by the accounting management unit. The information collection unit collects disaster information. The information distribution unit notifies the disaster information collected by the information collection unit. The group chat unit provides chat tailored to specific needs. [Effects of the Invention]

[0007] The system according to this embodiment can streamline event management, accounting management, and the transmission of emergency information. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

[0011] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), or TPU (Tensor Processing Unit).

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0013] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, etc. The communication I / F manages communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

[0016] [First Embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] As shown in FIG. 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 2, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0023] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The neighborhood association management support system according to an embodiment of the present invention is a system that provides an AI agent for streamlining the operation of neighborhood associations. This neighborhood association management support system includes an event management system, digital accounting and document management, an emergency information distribution network, and communication and forum functions. The event management system uses AI to work in conjunction with calendar management tools and communication tools to automate date and time adjustments and reminder setting. This makes event management and participant coordination easier. For example, it can automatically adjust the schedule of meetings and events and send reminders to participants. Digital accounting and document management works in conjunction with online accounting tools and cloud storage to simplify accounting management and document distribution. This promotes the digitalization of accounting management and distributed materials, enabling efficient operation. For example, income and expenditure reports and accounting ledgers can be managed on the cloud, and necessary documents can be distributed online. The emergency information distribution network quickly notifies disaster information and safety confirmations through a message distribution service. This enables the rapid transmission of emergency information and ensures the safety of residents. For example, in the event of a disaster such as an earthquake or typhoon, information can be quickly shared and evacuation orders can be issued. The communication and forum functions support interaction among residents by providing group chats and bulletin boards tailored to specific needs. This promotes active communication among residents and strengthens community ties. For example, bulletin boards can be set up for event announcements and opinion exchange, allowing residents to share information. In this way, the neighborhood association management support system provides an AI agent to streamline neighborhood association operations and improve the safety and convenience of residents.

[0029] The neighborhood association management support system according to this embodiment comprises a date and time adjustment unit, a reminder setting unit, an accounting management unit, a document distribution unit, an information collection unit, an information distribution unit, and a group chat unit. The date and time adjustment unit performs date and time adjustments. For example, the date and time adjustment unit detects available time slots on a calendar and proposes the optimal date and time. The date and time adjustment unit can use AI to analyze the user's schedule and automatically adjust to the optimal date and time. The reminder setting unit notifies the user of the date and time adjusted by the date and time adjustment unit. For example, the reminder setting unit sends reminders via email or messaging apps. The reminder setting unit can use AI to optimize the timing of sending reminders based on the user's schedule. The accounting management unit performs accounting management. For example, the accounting management unit records and manages income and expenses. The accounting management unit can use AI to analyze income and expense data and achieve efficient accounting management. The document distribution unit distributes accounting information managed by the accounting management unit. For example, the document distribution unit shares accounting information using cloud storage. The document distribution unit can automatically distribute necessary documents using AI. The information gathering unit collects disaster information. For example, the information gathering unit collects weather information and disaster information. The information gathering unit can collect and analyze disaster information in real time using AI. The information distribution unit notifies the disaster information collected by the information gathering unit. The information distribution unit distributes disaster information, for example, via email or messaging apps. The information distribution unit can distribute disaster information quickly and accurately using AI. The group chat unit provides chat tailored to specific needs. For example, the group chat unit provides a chat function to support communication among residents. The group chat unit can automatically create chat groups tailored to user needs using AI. As a result, the neighborhood association management support system according to this embodiment can efficiently handle scheduling, reminder setting, accounting management, document distribution, disaster information collection, information distribution, and group chat.

[0030] The scheduling department is designed to streamline scheduling among neighborhood association members. Specifically, it collects each member's calendar information and detects their availability. For example, it can integrate with external calendar services to automatically retrieve each member's schedule, eliminating the need for manual schedule checks. Furthermore, the scheduling department uses AI to analyze each member's past schedule patterns and priorities to suggest the optimal date and time. For instance, the AI ​​learns past meeting attendance rates and time trends to automatically select the date and time that most members can attend. The scheduling department also collects member feedback on the suggested date and time and allows for readjustment. This enables the quick determination of a date and time that is convenient for everyone.

[0031] The Reminder Setting Unit is responsible for notifying members of the schedule determined by the Schedule Adjustment Unit. Specifically, it has the function of sending reminders via email and messaging apps. For example, it can send a reminder by email the day before a meeting and a notification via a messaging app on the day of the meeting. Furthermore, the Reminder Setting Unit uses AI to optimize the timing of sending reminders based on each member's schedule. For example, the AI ​​analyzes each member's past reminder opening history and behavioral patterns to send reminders at the most effective time. This ensures that members do not miss reminders and can be sure to keep track of their schedules. In addition, the Reminder Setting Unit also has the function to customize the content of reminders. For example, it can include detailed information such as the meeting agenda and necessary preparations. This allows members to prepare appropriately for the meeting.

[0032] The Accounting Department provides functions for efficiently managing the neighborhood association's income and expenses. Specifically, it records income and expenses, creates budgets, and manages payments. For example, it has a function to monitor the collection status of membership fees in real time and send reminders to members who have not paid. The Accounting Department also uses AI to analyze income and expense data and achieve efficient accounting management. For example, the AI ​​evaluates the appropriateness of the budget based on past income and expense data and makes suggestions to reduce unnecessary expenses. Furthermore, the Accounting Department has a function to visualize income and expense data. For example, it displays the income and expense situation visually using graphs and charts, making it easy for members to understand. This improves the transparency of accounting management and allows all members to understand the financial situation.

[0033] The Document Distribution Department is responsible for distributing accounting information and other important documents managed by the Accounting Management Department to its members. Specifically, it has the functionality to share documents using cloud storage. For example, it can integrate with cloud services to upload documents and send sharing links to members. The Document Distribution Department also has the functionality to automatically distribute necessary documents using AI. For example, it can automatically generate and send regularly distributed documents such as accounting reports and meeting minutes to members. Furthermore, the Document Distribution Department also has the functionality to manage document access permissions. For example, it can set viewing permissions for specific documents, ensuring that only necessary members can access them. This enables efficient document distribution while ensuring information security.

[0034] The Information Gathering Department is responsible for collecting disaster information and other important information related to the neighborhood association. Specifically, it has the function of collecting weather and disaster information in real time. For example, it obtains publicly available data from the Japan Meteorological Agency and disaster prevention organizations and provides it to the members of the neighborhood association. In addition, the Information Gathering Department uses AI to analyze the collected information and evaluate its importance and urgency. For example, the AI ​​analyzes weather data to predict typhoon paths and rainfall and assess the risk of disaster. Furthermore, the Information Gathering Department also has the function of organizing the collected information and making it easily accessible to members. For example, it provides the latest information through a dedicated website and app. This allows members to always be aware of the latest information and take appropriate action.

[0035] The Information Distribution Department is responsible for notifying members of disaster information and other important information collected by the Information Gathering Department. Specifically, it has the function of distributing information via email and messaging apps. For example, in the event of a disaster, it will quickly notify members and call for evacuation and safety checks. The Information Distribution Department also uses AI to optimize the timing and method of information distribution. For example, the AI ​​analyzes each member's past notification reception history and behavioral patterns to send notifications at the most effective time. Furthermore, the Information Distribution Department has the function of customizing the content of the information it distributes. For example, it will provide information on appropriate response methods and evacuation locations depending on the type and scale of the disaster. This allows members to receive information quickly and accurately and take appropriate action.

[0036] The Group Chat Department provides a chat function to support communication among members of neighborhood associations. Specifically, it creates chat groups tailored to specific needs, providing an environment where members can communicate freely. For example, chat groups can be created for various purposes, such as event planning or disaster communication. The Group Chat Department also has a function that automatically creates chat groups tailored to user needs using AI. For example, the AI ​​analyzes members' profile information and past chat history and suggests the most suitable chat group based on common interests and needs. Furthermore, the Group Chat Department has a function that organizes chat content, picks out important information, and notifies members. This allows members to communicate efficiently without missing necessary information.

[0037] The bulletin board section supports information sharing among residents. For example, it provides a bulletin board function for residents to post and share information. The bulletin board section can use AI to analyze posted content and automatically display relevant information. The bulletin board section provides a bulletin board for residents to announce events and exchange opinions. The bulletin board section can use AI to classify posted content and efficiently display relevant information. The bulletin board section provides a user-friendly interface to make it easier for residents to share information. The bulletin board section can use AI to analyze posted content and provide information tailored to residents' needs. This enables efficient information sharing among residents.

[0038] The reminder setting unit notifies the user of the date and time adjusted by the date and time adjustment unit. The reminder setting unit sends reminders, for example, via email or messaging apps. The reminder setting unit can use AI to optimize the timing of reminder sending based on the user's schedule. The reminder setting unit can also estimate the user's emotions and adjust the timing of reminder sending based on the estimated emotions. For example, if the user is stressed, the reminder setting unit can delay sending the reminder. If the user is relaxed, the reminder setting unit can speed up sending the reminder. If the user is in a hurry, the reminder setting unit can send the reminder immediately. This allows the reminder setting unit to provide the user with appropriate reminders by notifying them of the date and time adjusted by the date and time adjustment unit.

[0039] The Document Distribution Department distributes accounting information managed by the Accounting Management Department. The Document Distribution Department shares accounting information using, for example, cloud storage. The Document Distribution Department can automatically distribute necessary documents using AI. The Document Distribution Department provides a system for efficiently distributing accounting information managed by the Accounting Management Department. The Document Distribution Department has functions for the rapid and accurate distribution of accounting information. The Document Distribution Department can monitor the distribution status of accounting information in real time and adjust the distribution method as needed. This allows for efficient sharing of accounting information by having the Document Distribution Department distribute the accounting information managed by the Accounting Management Department.

[0040] The Information Distribution Department notifies those who have collected disaster information from the Information Gathering Department. The Information Distribution Department distributes disaster information, for example, via email or messaging apps. The Information Distribution Department can use AI to distribute disaster information quickly and accurately. The Information Distribution Department provides a system for efficiently distributing disaster information. The Information Distribution Department can monitor the distribution status of disaster information in real time and adjust the distribution method as needed. The Information Distribution Department is equipped with functions for rapid distribution of disaster information. This enables rapid information sharing by having the Information Distribution Department notify those who have collected disaster information from the Information Gathering Department.

[0041] The group chat section provides chats tailored to specific needs. For example, it provides chat functions to support communication among residents. The group chat section can use AI to automatically create chat groups that meet user needs. The group chat section provides a user-friendly interface to facilitate information sharing among residents. The group chat section provides chat functions for residents to announce events and exchange opinions. The group chat section can use AI to analyze chat content and automatically display relevant information. By providing chats tailored to specific needs, it promotes active interaction among residents.

[0042] The scheduling unit analyzes the user's past participation history and proposes the optimal date and time. For example, the scheduling unit proposes the optimal date and time based on the date and time the user has frequently participated in the past. The scheduling unit prioritizes specific days of the week and time slots based on the user's past participation history. The scheduling unit analyzes the user's past participation history and proposes the date and time that is most convenient for the user to participate. In this way, the optimal date and time can be proposed by analyzing the user's past participation history.

[0043] The scheduling unit automatically detects the user's calendar availability and selects the optimal date and time during scheduling. For example, the scheduling unit scans the user's calendar and automatically detects availability. Based on the user's calendar availability, the scheduling unit proposes the optimal date and time. The scheduling unit updates the user's calendar availability in real time and selects the optimal date and time. This allows the system to automatically detect the user's calendar availability and select the optimal date and time.

[0044] The reminder setting unit analyzes the user's past reminder history when setting a reminder and selects the optimal sending method. For example, the reminder setting unit analyzes the user's past reminder history and selects the optimal sending method. The reminder setting unit suggests an effective sending method based on the user's past reminder history. The reminder setting unit selects the optimal sending method based on the user's past reminder history. This allows the optimal sending method to be selected by analyzing the user's past reminder history.

[0045] The reminder setting unit customizes the content of reminders based on the user's current schedule when a reminder is set. For example, the reminder setting unit customizes the content of reminders based on the user's current schedule. The reminder setting unit optimizes the content of reminders, taking the user's current schedule into consideration. The reminder setting unit updates the user's current schedule in real time and customizes the content of reminders accordingly. This allows for the provision of more appropriate reminders by customizing the content of reminders based on the user's current schedule.

[0046] The Accounting Management Department analyzes past accounting data and proposes the optimal management method during accounting management. For example, the Accounting Management Department analyzes past accounting data and proposes the optimal management method. The Accounting Management Department proposes effective management methods based on past accounting data. The Accounting Management Department selects the optimal management method based on past accounting data. Thus, by analyzing past accounting data, the department can propose the optimal management method.

[0047] The Accounting Management Department customizes the management methods based on the user's current financial situation during accounting management. For example, the Accounting Management Department customizes the management methods based on the user's current financial situation. The Accounting Management Department optimizes the management methods considering the user's current financial situation. The Accounting Management Department updates the user's current financial situation in real time and customizes the management methods accordingly. This allows for more appropriate accounting management by customizing the management methods based on the user's current financial situation.

[0048] The Information Gathering Department analyzes past disaster information during information gathering and proposes the optimal collection method. For example, the Information Gathering Department analyzes past disaster information and proposes the optimal collection method. The Information Gathering Department proposes effective collection methods based on past disaster information. The Information Gathering Department selects the optimal collection method based on past disaster information. Thus, by analyzing past disaster information, it can propose the optimal collection method.

[0049] The information gathering unit customizes the collected information based on the user's current situation. For example, the information gathering unit customizes the collected information based on the user's current situation. The information gathering unit optimizes the collected information considering the user's current situation. The information gathering unit updates the user's current situation in real time and customizes the collected information. This makes it possible to collect more appropriate information by customizing the collected information based on the user's current situation.

[0050] The information distribution department analyzes past distribution history and proposes the optimal distribution method when distributing information. For example, the information distribution department analyzes past distribution history and proposes the optimal distribution method. The information distribution department proposes an effective distribution method based on past distribution history. The information distribution department selects the optimal distribution method based on past distribution history. This allows the department to propose the optimal distribution method by analyzing past distribution history.

[0051] The information distribution unit customizes the content of the information distributed based on the user's current situation. For example, the information distribution unit customizes the content based on the user's current situation. The information distribution unit optimizes the content of the information distributed, taking the user's current situation into consideration. The information distribution unit updates the user's current situation in real time and customizes the content of the information distributed. This makes it possible to distribute more appropriate information by customizing the content of the information distributed based on the user's current situation.

[0052] The group chat team analyzes past chat history during group chats and proposes the optimal chat method. For example, the group chat team analyzes past chat history and proposes the most effective chat method. Based on past chat history, the group chat team proposes an effective chat method. Based on past chat history, the group chat team selects the optimal chat method. This allows the team to propose the most effective chat method by analyzing past chat history.

[0053] The group chat function customizes chat content based on the user's current needs during group chats. For example, the group chat function customizes chat content based on the user's current needs. The group chat function optimizes chat content considering the user's current needs. The group chat function updates the user's current needs in real time and customizes chat content accordingly. This allows for more appropriate chats by customizing chat content based on the user's current needs.

[0054] The bulletin board department analyzes past bulletin board history when a user accesses the bulletin board and proposes the optimal way to use it. For example, the bulletin board department analyzes past bulletin board history and proposes the optimal way to use it. The bulletin board department proposes effective bulletin board usage methods based on past bulletin board history. The bulletin board department selects the optimal way to use the bulletin board based on past bulletin board history. This allows the bulletin board department to propose the optimal way to use the bulletin board by analyzing past bulletin board history.

[0055] The bulletin board section customizes the bulletin board content based on the user's current needs when the bulletin board is used. For example, the bulletin board section customizes the bulletin board content based on the user's current needs. The bulletin board section optimizes the bulletin board content considering the user's current needs. The bulletin board section updates the user's current needs in real time and customizes the bulletin board content. This allows for more appropriate use of the bulletin board by customizing the bulletin board content based on the user's current needs.

[0056] The bulletin board section proposes the optimal bulletin board usage method when the user is using the bulletin board, taking into account the user's geographical location information. For example, the bulletin board section proposes the optimal bulletin board usage method based on the user's current location. The bulletin board section proposes an effective bulletin board usage method by taking into account the user's geographical location information. The bulletin board section updates the user's geographical location information in real time and selects the optimal bulletin board usage method. In this way, it can propose the optimal bulletin board usage method by taking into account the user's geographical location information.

[0057] The bulletin board section analyzes the user's social media activity when they use the bulletin board and provides relevant bulletin board content. For example, the bulletin board section analyzes the user's social media activity and provides relevant bulletin board content. The bulletin board section suggests bulletin board content of interest based on the user's social media activity. The bulletin board section analyzes the user's social media activity in real time and provides optimal bulletin board content. This allows the bulletin board section to provide relevant bulletin board content by analyzing the user's social media activity.

[0058] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0059] The scheduling unit can monitor the user's health status and adjust schedules based on that status. For example, if a user is fatigued, the date and time of an important meeting or event can be postponed. If the user is healthy, early morning or late evening schedules can be suggested. Furthermore, if the user is ill, dates and times that allow for remote participation can be suggested. This enables scheduling that takes the user's health into consideration.

[0060] The reminder setting section can analyze the user's past reminder response history and select the optimal reminder delivery method. For example, if a user has a history of responding well to email reminders, email reminder delivery can be prioritized. If a user has a history of responding well to reminders via messaging apps, reminder delivery via messaging apps can be prioritized. Furthermore, if a user has a history of responding well to voice notifications, voice notifications can be used. This allows the system to provide the optimal reminder delivery method based on the user's past reminder response history.

[0061] The accounting department can set financial goals for users and provide advice to help them achieve those goals. For example, if a user sets a savings goal, the department can suggest a monthly savings amount. If a user sets a spending reduction goal, the department can provide advice on how to reduce unnecessary spending. Furthermore, if a user sets an investment goal, the department can suggest investment options that match their risk tolerance. In this way, the department can support users in achieving their financial goals.

[0062] The information gathering unit can collect information based on the user's areas of interest. For example, if a user is interested in disaster prevention, it can prioritize collecting information related to disaster prevention. If a user is interested in health, it can prioritize collecting information related to health. Also, if a user is interested in local events, it can prioritize collecting information related to local events. This makes it possible to collect information according to the user's areas of interest.

[0063] The information distribution unit can adjust the information distribution method according to the user's communication environment. For example, if the user is in a high-speed internet environment, detailed information including videos and images can be distributed. If the user is in a low-speed internet environment, concise text-based information can be distributed. Furthermore, if the user is in an environment with communication restrictions, information distribution can be performed with reduced data volume. This enables optimal information distribution tailored to the user's communication environment.

[0064] The following briefly describes the processing flow for example form 1.

[0065] Step 1: The scheduling unit performs scheduling. For example, the scheduling unit detects available time slots on the calendar and suggests the optimal date and time. The scheduling unit can use AI to analyze the user's schedule and automatically adjust to the optimal date and time. Step 2: The reminder setting unit notifies the user of the date and time adjusted by the date and time adjustment unit. The reminder setting unit sends the reminder, for example, via email or a messaging app. The reminder setting unit can use AI to optimize the timing of sending reminders based on the user's schedule. Step 3: The Accounting Department performs accounting management. For example, the Accounting Department records and manages income and expenses. The Accounting Department can use AI to analyze income and expense data and achieve efficient accounting management. Step 4: The document distribution department distributes accounting information managed by the accounting department. The document distribution department can share accounting information using, for example, cloud storage. The document distribution department can use AI to automatically distribute necessary documents. Step 5: The Information Gathering Unit collects disaster information. The Information Gathering Unit collects, for example, weather information and disaster information. The Information Gathering Unit can use AI to collect and analyze disaster information in real time. Step 6: The Information Distribution Department notifies the disaster information collected by the Information Collection Department. The Information Distribution Department distributes the disaster information, for example, via email or messaging apps. The Information Distribution Department can use AI to distribute disaster information quickly and accurately. Step 7: The group chat section provides chats tailored to specific needs. For example, the group chat section provides chat functions to support communication among residents. The group chat section can use AI to automatically create chat groups that meet the user's needs.

[0066] (Example of form 2) The neighborhood association management support system according to an embodiment of the present invention is a system that provides an AI agent for streamlining the operation of neighborhood associations. This neighborhood association management support system includes an event management system, digital accounting and document management, an emergency information distribution network, and communication and forum functions. The event management system uses AI to work in conjunction with calendar management tools and communication tools to automate date and time adjustments and reminder setting. This makes event management and participant coordination easier. For example, it can automatically adjust the schedule of meetings and events and send reminders to participants. Digital accounting and document management works in conjunction with online accounting tools and cloud storage to simplify accounting management and document distribution. This promotes the digitalization of accounting management and distributed materials, enabling efficient operation. For example, income and expenditure reports and accounting ledgers can be managed on the cloud, and necessary documents can be distributed online. The emergency information distribution network quickly notifies disaster information and safety confirmations through a message distribution service. This enables the rapid transmission of emergency information and ensures the safety of residents. For example, in the event of a disaster such as an earthquake or typhoon, information can be quickly shared and evacuation orders can be issued. The communication and forum functions support interaction among residents by providing group chats and bulletin boards tailored to specific needs. This promotes active communication among residents and strengthens community ties. For example, bulletin boards can be set up for event announcements and opinion exchange, allowing residents to share information. In this way, the neighborhood association management support system provides an AI agent to streamline neighborhood association operations and improve the safety and convenience of residents.

[0067] The neighborhood association management support system according to this embodiment comprises a date and time adjustment unit, a reminder setting unit, an accounting management unit, a document distribution unit, an information collection unit, an information distribution unit, and a group chat unit. The date and time adjustment unit performs date and time adjustments. For example, the date and time adjustment unit detects available time slots on a calendar and proposes the optimal date and time. The date and time adjustment unit can use AI to analyze the user's schedule and automatically adjust to the optimal date and time. The reminder setting unit notifies the user of the date and time adjusted by the date and time adjustment unit. For example, the reminder setting unit sends reminders via email or messaging apps. The reminder setting unit can use AI to optimize the timing of sending reminders based on the user's schedule. The accounting management unit performs accounting management. For example, the accounting management unit records and manages income and expenses. The accounting management unit can use AI to analyze income and expense data and achieve efficient accounting management. The document distribution unit distributes accounting information managed by the accounting management unit. For example, the document distribution unit shares accounting information using cloud storage. The document distribution unit can automatically distribute necessary documents using AI. The information gathering unit collects disaster information. For example, the information gathering unit collects weather information and disaster information. The information gathering unit can collect and analyze disaster information in real time using AI. The information distribution unit notifies the disaster information collected by the information gathering unit. The information distribution unit distributes disaster information, for example, via email or messaging apps. The information distribution unit can distribute disaster information quickly and accurately using AI. The group chat unit provides chat tailored to specific needs. For example, the group chat unit provides a chat function to support communication among residents. The group chat unit can automatically create chat groups tailored to user needs using AI. As a result, the neighborhood association management support system according to this embodiment can efficiently handle scheduling, reminder setting, accounting management, document distribution, disaster information collection, information distribution, and group chat.

[0068] The scheduling department is designed to streamline scheduling among neighborhood association members. Specifically, it collects each member's calendar information and detects their availability. For example, it can integrate with external calendar services to automatically retrieve each member's schedule, eliminating the need for manual schedule checks. Furthermore, the scheduling department uses AI to analyze each member's past schedule patterns and priorities to suggest the optimal date and time. For instance, the AI ​​learns past meeting attendance rates and time trends to automatically select the date and time that most members can attend. The scheduling department also collects member feedback on the suggested date and time and allows for readjustment. This enables the quick determination of a date and time that is convenient for everyone.

[0069] The Reminder Setting Unit is responsible for notifying members of the schedule determined by the Schedule Adjustment Unit. Specifically, it has the function of sending reminders via email and messaging apps. For example, it can send a reminder by email the day before a meeting and a notification via a messaging app on the day of the meeting. Furthermore, the Reminder Setting Unit uses AI to optimize the timing of sending reminders based on each member's schedule. For example, the AI ​​analyzes each member's past reminder opening history and behavioral patterns to send reminders at the most effective time. This ensures that members do not miss reminders and can be sure to keep track of their schedules. In addition, the Reminder Setting Unit also has the function to customize the content of reminders. For example, it can include detailed information such as the meeting agenda and necessary preparations. This allows members to prepare appropriately for the meeting.

[0070] The Accounting Department provides functions for efficiently managing the neighborhood association's income and expenses. Specifically, it records income and expenses, creates budgets, and manages payments. For example, it has a function to monitor the collection status of membership fees in real time and send reminders to members who have not paid. The Accounting Department also uses AI to analyze income and expense data and achieve efficient accounting management. For example, the AI ​​evaluates the appropriateness of the budget based on past income and expense data and makes suggestions to reduce unnecessary expenses. Furthermore, the Accounting Department has a function to visualize income and expense data. For example, it displays the income and expense situation visually using graphs and charts, making it easy for members to understand. This improves the transparency of accounting management and allows all members to understand the financial situation.

[0071] The Document Distribution Department is responsible for distributing accounting information and other important documents managed by the Accounting Management Department to its members. Specifically, it has the functionality to share documents using cloud storage. For example, it can integrate with cloud services to upload documents and send sharing links to members. The Document Distribution Department also has the functionality to automatically distribute necessary documents using AI. For example, it can automatically generate and send regularly distributed documents such as accounting reports and meeting minutes to members. Furthermore, the Document Distribution Department also has the functionality to manage document access permissions. For example, it can set viewing permissions for specific documents, ensuring that only necessary members can access them. This enables efficient document distribution while ensuring information security.

[0072] The Information Gathering Department is responsible for collecting disaster information and other important information related to the neighborhood association. Specifically, it has the function of collecting weather and disaster information in real time. For example, it obtains publicly available data from the Japan Meteorological Agency and disaster prevention organizations and provides it to the members of the neighborhood association. In addition, the Information Gathering Department uses AI to analyze the collected information and evaluate its importance and urgency. For example, the AI ​​analyzes weather data to predict typhoon paths and rainfall and assess the risk of disaster. Furthermore, the Information Gathering Department also has the function of organizing the collected information and making it easily accessible to members. For example, it provides the latest information through a dedicated website and app. This allows members to always be aware of the latest information and take appropriate action.

[0073] The Information Distribution Department is responsible for notifying members of disaster information and other important information collected by the Information Gathering Department. Specifically, it has the function of distributing information via email and messaging apps. For example, in the event of a disaster, it will quickly notify members and call for evacuation and safety checks. The Information Distribution Department also uses AI to optimize the timing and method of information distribution. For example, the AI ​​analyzes each member's past notification reception history and behavioral patterns to send notifications at the most effective time. Furthermore, the Information Distribution Department has the function of customizing the content of the information it distributes. For example, it will provide information on appropriate response methods and evacuation locations depending on the type and scale of the disaster. This allows members to receive information quickly and accurately and take appropriate action.

[0074] The Group Chat Department provides a chat function to support communication among members of neighborhood associations. Specifically, it creates chat groups tailored to specific needs, providing an environment where members can communicate freely. For example, chat groups can be created for various purposes, such as event planning or disaster communication. The Group Chat Department also has a function that automatically creates chat groups tailored to user needs using AI. For example, the AI ​​analyzes members' profile information and past chat history and suggests the most suitable chat group based on common interests and needs. Furthermore, the Group Chat Department has a function that organizes chat content, picks out important information, and notifies members. This allows members to communicate efficiently without missing necessary information.

[0075] The bulletin board section supports information sharing among residents. For example, it provides a bulletin board function for residents to post and share information. The bulletin board section can use AI to analyze posted content and automatically display relevant information. The bulletin board section provides a bulletin board for residents to announce events and exchange opinions. The bulletin board section can use AI to classify posted content and efficiently display relevant information. The bulletin board section provides a user-friendly interface to make it easier for residents to share information. The bulletin board section can use AI to analyze posted content and provide information tailored to residents' needs. This enables efficient information sharing among residents.

[0076] The reminder setting unit notifies the user of the date and time adjusted by the date and time adjustment unit. The reminder setting unit sends reminders, for example, via email or messaging apps. The reminder setting unit can use AI to optimize the timing of reminder sending based on the user's schedule. The reminder setting unit can also estimate the user's emotions and adjust the timing of reminder sending based on the estimated emotions. For example, if the user is stressed, the reminder setting unit can delay sending the reminder. If the user is relaxed, the reminder setting unit can speed up sending the reminder. If the user is in a hurry, the reminder setting unit can send the reminder immediately. This allows the reminder setting unit to provide the user with appropriate reminders by notifying them of the date and time adjusted by the date and time adjustment unit.

[0077] The Document Distribution Department distributes accounting information managed by the Accounting Management Department. The Document Distribution Department shares accounting information using, for example, cloud storage. The Document Distribution Department can automatically distribute necessary documents using AI. The Document Distribution Department provides a system for efficiently distributing accounting information managed by the Accounting Management Department. The Document Distribution Department has functions for the rapid and accurate distribution of accounting information. The Document Distribution Department can monitor the distribution status of accounting information in real time and adjust the distribution method as needed. This allows for efficient sharing of accounting information by having the Document Distribution Department distribute the accounting information managed by the Accounting Management Department.

[0078] The Information Distribution Department notifies those who have collected disaster information from the Information Gathering Department. The Information Distribution Department distributes disaster information, for example, via email or messaging apps. The Information Distribution Department can use AI to distribute disaster information quickly and accurately. The Information Distribution Department provides a system for efficiently distributing disaster information. The Information Distribution Department can monitor the distribution status of disaster information in real time and adjust the distribution method as needed. The Information Distribution Department is equipped with functions for rapid distribution of disaster information. This enables rapid information sharing by having the Information Distribution Department notify those who have collected disaster information from the Information Gathering Department.

[0079] The group chat section provides chats tailored to specific needs. For example, it provides chat functions to support communication among residents. The group chat section can use AI to automatically create chat groups that meet user needs. The group chat section provides a user-friendly interface to facilitate information sharing among residents. The group chat section provides chat functions for residents to announce events and exchange opinions. The group chat section can use AI to analyze chat content and automatically display relevant information. By providing chats tailored to specific needs, it promotes active interaction among residents.

[0080] The scheduling unit estimates the user's emotions and determines scheduling priorities based on those emotions. For example, if the user is stressed, the scheduling unit prioritizes scheduling important meetings or events. If the user is relaxed, the scheduling unit suggests flexible schedules. If the user is in a hurry, the scheduling unit prioritizes scheduling the earliest possible date and time. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This allows for more appropriate scheduling by determining scheduling priorities based on the user's emotions.

[0081] The scheduling unit analyzes the user's past participation history and proposes the optimal date and time. For example, the scheduling unit proposes the optimal date and time based on the date and time the user has frequently participated in the past. The scheduling unit prioritizes specific days of the week and time slots based on the user's past participation history. The scheduling unit analyzes the user's past participation history and proposes the date and time that is most convenient for the user to participate. In this way, the optimal date and time can be proposed by analyzing the user's past participation history.

[0082] The scheduling unit automatically detects the user's calendar availability and selects the optimal date and time during scheduling. For example, the scheduling unit scans the user's calendar and automatically detects availability. Based on the user's calendar availability, the scheduling unit proposes the optimal date and time. The scheduling unit updates the user's calendar availability in real time and selects the optimal date and time. This allows the system to automatically detect the user's calendar availability and select the optimal date and time.

[0083] The reminder setting unit estimates the user's emotions and adjusts the timing of reminder delivery based on the estimated emotions. For example, if the user is stressed, the reminder setting unit will delay sending the reminder. If the user is relaxed, the reminder setting unit will speed up sending the reminder. If the user is in a hurry, the reminder setting unit will send the reminder immediately. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This allows for the provision of more appropriate reminders by adjusting the timing of reminder delivery based on the user's emotions.

[0084] The reminder setting unit analyzes the user's past reminder history when setting a reminder and selects the optimal sending method. For example, the reminder setting unit analyzes the user's past reminder history and selects the optimal sending method. The reminder setting unit suggests an effective sending method based on the user's past reminder history. The reminder setting unit selects the optimal sending method based on the user's past reminder history. This allows the optimal sending method to be selected by analyzing the user's past reminder history.

[0085] The reminder setting unit customizes the content of reminders based on the user's current schedule when a reminder is set. For example, the reminder setting unit customizes the content of reminders based on the user's current schedule. The reminder setting unit optimizes the content of reminders, taking the user's current schedule into consideration. The reminder setting unit updates the user's current schedule in real time and customizes the content of reminders accordingly. This allows for the provision of more appropriate reminders by customizing the content of reminders based on the user's current schedule.

[0086] The accounting department estimates the user's emotions and prioritizes accounting management based on those emotions. For example, if the user is stressed, the accounting department prioritizes important accounting management. If the user is relaxed, the accounting department suggests flexible accounting management. If the user is in a hurry, the accounting department prioritizes the fastest accounting management. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This allows for more appropriate accounting management by prioritizing accounting management based on the user's emotions.

[0087] The Accounting Management Department analyzes past accounting data and proposes the optimal management method during accounting management. For example, the Accounting Management Department analyzes past accounting data and proposes the optimal management method. The Accounting Management Department proposes effective management methods based on past accounting data. The Accounting Management Department selects the optimal management method based on past accounting data. Thus, by analyzing past accounting data, the department can propose the optimal management method.

[0088] The Accounting Management Department customizes the management methods based on the user's current financial situation during accounting management. For example, the Accounting Management Department customizes the management methods based on the user's current financial situation. The Accounting Management Department optimizes the management methods considering the user's current financial situation. The Accounting Management Department updates the user's current financial situation in real time and customizes the management methods accordingly. This allows for more appropriate accounting management by customizing the management methods based on the user's current financial situation.

[0089] The information gathering unit estimates the user's emotions and determines the priority of information gathering based on the estimated emotions. For example, if the user is stressed, the information gathering unit prioritizes gathering important information. If the user is relaxed, the information gathering unit suggests flexible information gathering. If the user is in a hurry, the information gathering unit prioritizes gathering the fastest information. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This allows for more appropriate information gathering by determining the priority of information gathering based on the user's emotions.

[0090] The Information Gathering Department analyzes past disaster information during information gathering and proposes the optimal collection method. For example, the Information Gathering Department analyzes past disaster information and proposes the optimal collection method. The Information Gathering Department proposes effective collection methods based on past disaster information. The Information Gathering Department selects the optimal collection method based on past disaster information. Thus, by analyzing past disaster information, it can propose the optimal collection method.

[0091] The information gathering unit customizes the collected information based on the user's current situation. For example, the information gathering unit customizes the collected information based on the user's current situation. The information gathering unit optimizes the collected information considering the user's current situation. The information gathering unit updates the user's current situation in real time and customizes the collected information. This makes it possible to collect more appropriate information by customizing the collected information based on the user's current situation.

[0092] The information delivery unit estimates the user's emotions and determines the priority of information delivery based on the estimated emotions. For example, if the user is stressed, the information delivery unit will prioritize important information delivery. If the user is relaxed, the information delivery unit will suggest flexible information delivery. If the user is in a hurry, the information delivery unit will prioritize the fastest information delivery. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This allows for more appropriate information delivery by determining the priority of information delivery based on the user's emotions.

[0093] The information distribution department analyzes past distribution history and proposes the optimal distribution method when distributing information. For example, the information distribution department analyzes past distribution history and proposes the optimal distribution method. The information distribution department proposes an effective distribution method based on past distribution history. The information distribution department selects the optimal distribution method based on past distribution history. This allows the department to propose the optimal distribution method by analyzing past distribution history.

[0094] The information distribution unit customizes the content of the information distributed based on the user's current situation. For example, the information distribution unit customizes the content based on the user's current situation. The information distribution unit optimizes the content of the information distributed, taking the user's current situation into consideration. The information distribution unit updates the user's current situation in real time and customizes the content of the information distributed. This makes it possible to distribute more appropriate information by customizing the content of the information distributed based on the user's current situation.

[0095] The group chat function estimates the user's emotions and prioritizes chats based on those emotions. For example, if the user is stressed, the group chat function prioritizes important chats. If the user is relaxed, the group chat function suggests flexible chats. If the user is in a hurry, the group chat function prioritizes the fastest chats. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This allows for more appropriate chats by prioritizing chats based on the user's emotions.

[0096] The group chat team analyzes past chat history during group chats and proposes the optimal chat method. For example, the group chat team analyzes past chat history and proposes the most effective chat method. Based on past chat history, the group chat team proposes an effective chat method. Based on past chat history, the group chat team selects the optimal chat method. This allows the team to propose the most effective chat method by analyzing past chat history.

[0097] The group chat function customizes chat content based on the user's current needs during group chats. For example, the group chat function customizes chat content based on the user's current needs. The group chat function optimizes chat content considering the user's current needs. The group chat function updates the user's current needs in real time and customizes chat content accordingly. This allows for more appropriate chats by customizing chat content based on the user's current needs.

[0098] The bulletin board section estimates the user's emotions and prioritizes bulletin boards based on the estimated emotions. For example, if the user is stressed, the bulletin board section prioritizes important bulletin boards. If the user is relaxed, the bulletin board section suggests flexible bulletin boards. If the user is in a hurry, the bulletin board section prioritizes the fastest bulletin boards. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This allows for more appropriate use of the bulletin board by prioritizing bulletin boards based on the user's emotions.

[0099] The bulletin board department analyzes past bulletin board history when a user accesses the bulletin board and proposes the optimal way to use it. For example, the bulletin board department analyzes past bulletin board history and proposes the optimal way to use it. The bulletin board department proposes effective bulletin board usage methods based on past bulletin board history. The bulletin board department selects the optimal way to use the bulletin board based on past bulletin board history. This allows the bulletin board department to propose the optimal way to use the bulletin board by analyzing past bulletin board history.

[0100] The bulletin board section customizes the bulletin board content based on the user's current needs when the bulletin board is used. For example, the bulletin board section customizes the bulletin board content based on the user's current needs. The bulletin board section optimizes the bulletin board content considering the user's current needs. The bulletin board section updates the user's current needs in real time and customizes the bulletin board content. This allows for more appropriate use of the bulletin board by customizing the bulletin board content based on the user's current needs.

[0101] The bulletin board section estimates the user's emotions and adjusts the bulletin board's notification method based on the estimated emotions. For example, if the user is stressed, the bulletin board section will select a simple notification method. If the user is relaxed, the bulletin board section will select a detailed notification method. If the user is in a hurry, the bulletin board section will select a rapid notification method. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This allows for more appropriate use of the bulletin board by adjusting the notification method based on the user's emotions.

[0102] The bulletin board section proposes the optimal bulletin board usage method when the user is using the bulletin board, taking into account the user's geographical location information. For example, the bulletin board section proposes the optimal bulletin board usage method based on the user's current location. The bulletin board section proposes an effective bulletin board usage method by taking into account the user's geographical location information. The bulletin board section updates the user's geographical location information in real time and selects the optimal bulletin board usage method. In this way, it can propose the optimal bulletin board usage method by taking into account the user's geographical location information.

[0103] The bulletin board section analyzes the user's social media activity when they use the bulletin board and provides relevant bulletin board content. For example, the bulletin board section analyzes the user's social media activity and provides relevant bulletin board content. The bulletin board section suggests bulletin board content of interest based on the user's social media activity. The bulletin board section analyzes the user's social media activity in real time and provides optimal bulletin board content. This allows the bulletin board section to provide relevant bulletin board content by analyzing the user's social media activity.

[0104] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0105] The scheduling unit can monitor the user's health status and adjust schedules based on that status. For example, if a user is fatigued, the date and time of an important meeting or event can be postponed. If the user is healthy, early morning or late evening schedules can be suggested. Furthermore, if the user is ill, dates and times that allow for remote participation can be suggested. This enables scheduling that takes the user's health into consideration.

[0106] The reminder setting section can analyze the user's past reminder response history and select the optimal reminder delivery method. For example, if a user has a history of responding well to email reminders, email reminder delivery can be prioritized. If a user has a history of responding well to reminders via messaging apps, reminder delivery via messaging apps can be prioritized. Furthermore, if a user has a history of responding well to voice notifications, voice notifications can be used. This allows the system to provide the optimal reminder delivery method based on the user's past reminder response history.

[0107] The accounting department can set financial goals for users and provide advice to help them achieve those goals. For example, if a user sets a savings goal, the department can suggest a monthly savings amount. If a user sets a spending reduction goal, the department can provide advice on how to reduce unnecessary spending. Furthermore, if a user sets an investment goal, the department can suggest investment options that match their risk tolerance. In this way, the department can support users in achieving their financial goals.

[0108] The information gathering unit can collect information based on the user's areas of interest. For example, if a user is interested in disaster prevention, it can prioritize collecting information related to disaster prevention. If a user is interested in health, it can prioritize collecting information related to health. Also, if a user is interested in local events, it can prioritize collecting information related to local events. This makes it possible to collect information according to the user's areas of interest.

[0109] The information distribution unit can adjust the information distribution method according to the user's communication environment. For example, if the user is in a high-speed internet environment, detailed information including videos and images can be distributed. If the user is in a low-speed internet environment, concise text-based information can be distributed. Furthermore, if the user is in an environment with communication restrictions, information distribution can be performed with reduced data volume. This enables optimal information distribution tailored to the user's communication environment.

[0110] The group chat function can estimate a user's emotions and suggest chat topics based on those estimations. For example, if a user is stressed, it can suggest relaxing topics. If a user is relaxed, it can suggest fun topics. Furthermore, if a user is in a hurry, it can prioritize suggesting important topics. This allows the system to provide appropriate chat topics tailored to the user's emotions.

[0111] The bulletin board section can estimate the user's emotions and adjust the content displayed based on those emotions. For example, if a user is stressed, positive content can be prioritized. If a user is relaxed, interesting content can be prioritized. Furthermore, if a user is in a hurry, important information can be prioritized. This enables optimal bulletin board display tailored to the user's emotions.

[0112] The scheduling unit can estimate the user's emotions and customize the suggested scheduling based on those emotions. For example, if the user is feeling stressed, it can suggest a time when they can relax. If the user is relaxed, it can suggest a time when they can concentrate better. Also, if the user is in a hurry, it can suggest the earliest possible date and time. This makes it possible to schedule the meeting at the most appropriate time according to the user's emotions.

[0113] The reminder setting section can estimate the user's emotions and customize the reminder content based on those emotions. For example, if the user is stressed, the reminder content can be made concise. If the user is relaxed, detailed reminder content can be provided. Also, if the user is in a hurry, only the important points can be included in the reminder. This allows for the provision of optimal reminder content tailored to the user's emotions.

[0114] The information gathering unit can estimate the user's emotions and adjust its information gathering methods based on those estimates. For example, if the user is stressed, it can prioritize collecting important information. If the user is relaxed, it can prioritize collecting interesting information. If the user is in a hurry, it can prioritize information that can be collected quickly. This enables optimal information gathering tailored to the user's emotions.

[0115] The following briefly describes the processing flow for example form 2.

[0116] Step 1: The scheduling unit performs scheduling. For example, the scheduling unit detects available time slots on the calendar and suggests the optimal date and time. The scheduling unit can use AI to analyze the user's schedule and automatically adjust to the optimal date and time. Step 2: The reminder setting unit notifies the user of the date and time adjusted by the date and time adjustment unit. The reminder setting unit sends the reminder, for example, via email or a messaging app. The reminder setting unit can use AI to optimize the timing of sending reminders based on the user's schedule. Step 3: The Accounting Department performs accounting management. For example, the Accounting Department records and manages income and expenses. The Accounting Department can use AI to analyze income and expense data and achieve efficient accounting management. Step 4: The document distribution department distributes accounting information managed by the accounting department. The document distribution department can share accounting information using, for example, cloud storage. The document distribution department can use AI to automatically distribute necessary documents. Step 5: The Information Gathering Unit collects disaster information. The Information Gathering Unit collects, for example, weather information and disaster information. The Information Gathering Unit can use AI to collect and analyze disaster information in real time. Step 6: The Information Distribution Department notifies the disaster information collected by the Information Collection Department. The Information Distribution Department distributes the disaster information, for example, via email or messaging apps. The Information Distribution Department can use AI to distribute disaster information quickly and accurately. Step 7: The group chat section provides chats tailored to specific needs. For example, the group chat section provides chat functions to support communication among residents. The group chat section can use AI to automatically create chat groups that meet the user's needs.

[0117] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0118] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0119] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0120] Each of the multiple elements described above, including the date and time adjustment unit, reminder setting unit, accounting management unit, document distribution unit, information collection unit, information distribution unit, group chat unit, and bulletin board unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the date and time adjustment unit is implemented by the control unit 46A of the smart device 14, which detects available time on the calendar and proposes the optimal date and time. The reminder setting unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which notifies the adjusted date and time. The accounting management unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which records and manages income and expenses. The document distribution unit is implemented by, for example, the control unit 46A of the smart device 14, which shares accounting information using cloud storage. The information collection unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which collects disaster information. The information distribution unit is implemented by, for example, the control unit 46A of the smart device 14, which notifies the collected disaster information. The group chat section is implemented, for example, by the control unit 46A of the smart device 14, and supports communication among residents. The bulletin board section is implemented, for example, by the specific processing unit 290 of the data processing device 12, and provides a bulletin board function for residents to post and share information. The correspondence between each section and the devices and control units is not limited to the examples described above, and various modifications are possible.

[0121] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0122] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0123] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0124] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0125] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0126] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0127] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0128] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0129] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0130] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0131] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0132] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0133] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0134] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0135] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0136] Each of the multiple elements described above, including the date and time adjustment unit, reminder setting unit, accounting management unit, document distribution unit, information collection unit, information distribution unit, group chat unit, and bulletin board unit, is implemented by, for example, at least one of the smart glasses 214 and the data processing unit 12. For example, the date and time adjustment unit is implemented by the control unit 46A of the smart glasses 214, which detects available time on the calendar and proposes the optimal date and time. The reminder setting unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which notifies the adjusted date and time. The accounting management unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which records and manages income and expenses. The document distribution unit is implemented by, for example, the control unit 46A of the smart glasses 214, which shares accounting information using cloud storage. The information collection unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which collects disaster information. The information distribution unit is implemented by, for example, the control unit 46A of the smart glasses 214, which notifies the collected disaster information. The group chat section is implemented, for example, by the control unit 46A of the smart glasses 214, and supports communication among residents. The bulletin board section is implemented, for example, by the specific processing unit 290 of the data processing device 12, and provides a bulletin board function for residents to post and share information. The correspondence between each section and the devices and control units is not limited to the examples described above, and various modifications are possible.

[0137] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0138] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0139] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0140] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0141] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0143] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0144] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0145] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0146] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0147] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0148] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0149] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0150] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0151] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0152] Each of the multiple elements described above, including the date and time adjustment unit, reminder setting unit, accounting management unit, document distribution unit, information collection unit, information distribution unit, group chat unit, and bulletin board unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the date and time adjustment unit is implemented by the control unit 46A of the headset terminal 314, which detects available time on the calendar and proposes the optimal date and time. The reminder setting unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which notifies the adjusted date and time. The accounting management unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which records and manages income and expenses. The document distribution unit is implemented by, for example, the control unit 46A of the headset terminal 314, which shares accounting information using cloud storage. The information collection unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which collects disaster information. The information distribution unit is implemented by, for example, the control unit 46A of the headset terminal 314, which notifies the collected disaster information. The group chat section is implemented, for example, by the control unit 46A of the headset terminal 314, and supports communication among residents. The bulletin board section is implemented, for example, by the specific processing unit 290 of the data processing device 12, and provides a bulletin board function for residents to post and share information. The correspondence between each section and the devices and control units is not limited to the examples described above, and various modifications are possible.

[0153] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0154] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0155] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0156] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0157] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0158] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0159] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0160] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0161] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0162] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0163] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0164] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0165] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0166] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0167] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0168] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0169] Each of the multiple elements described above, including the date and time adjustment unit, reminder setting unit, accounting management unit, document distribution unit, information collection unit, information distribution unit, group chat unit, and bulletin board unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the date and time adjustment unit is implemented by the control unit 46A of the robot 414, which detects available time on the calendar and proposes the optimal date and time. The reminder setting unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which notifies the adjusted date and time. The accounting management unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which records and manages income and expenses. The document distribution unit is implemented by, for example, the control unit 46A of the robot 414, which shares accounting information using cloud storage. The information collection unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which collects disaster information. The information distribution unit is implemented by, for example, the control unit 46A of the robot 414, which notifies the collected disaster information. The group chat section is implemented, for example, by the control unit 46A of the robot 414, and supports communication among residents. The bulletin board section is implemented, for example, by the specific processing unit 290 of the data processing device 12, and provides a bulletin board function for residents to post and share information. The correspondence between each section and the devices and control units is not limited to the examples described above, and various modifications are possible.

[0170] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0171] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0172] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0173] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0174] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0175] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0176] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0177] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0178] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0179] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0180] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0181] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0182] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0183] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0184] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0185] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0186] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0187] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0188] (Note 1) A date and time adjustment unit that adjusts the date and time, A reminder setting unit that notifies the date and time adjusted by the aforementioned date and time adjustment unit, The Accounting Management Department, which is responsible for accounting management, A document distribution unit that distributes accounting information managed by the aforementioned accounting management unit, The Information Gathering Department collects disaster information, The information distribution unit notifies the disaster information collected by the aforementioned information gathering unit, It includes a group chat section that provides chat tailored to specific needs. A system characterized by the following features. (Note 2) Equipped with a bulletin board section, The bulletin board section supports information sharing among residents. The system described in Appendix 1, characterized by the features described herein. (Note 3) The reminder setting unit is, The date and time adjusted by the date and time adjustment unit will be notified. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned document distribution unit, Distribute accounting information managed by the Accounting Department. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned information distribution unit, Disaster information collected by the Information Gathering Department will be notified. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned group chat section is We provide chat tailored to specific needs. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned date and time adjustment unit, It estimates the user's emotions and determines the priority of scheduling based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned date and time adjustment unit, We analyze the user's past participation history and suggest the optimal date and time. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned date and time adjustment unit, When scheduling a meeting, the system automatically detects the user's available time slots in their calendar and selects the most suitable date and time. The system described in Appendix 1, characterized by the features described herein. (Note 10) The reminder setting unit is, It estimates the user's emotions and adjusts the timing of sending reminders based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The reminder setting unit is, When setting a reminder, the system analyzes the user's past reminder history and selects the optimal sending method. The system described in Appendix 1, characterized by the features described herein. (Note 12) The reminder setting unit is, When setting a reminder, customize the reminder content based on the user's current schedule. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned accounting management department, It estimates user sentiment and determines accounting management priorities based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned accounting management department, During accounting management, we analyze past accounting data and propose the optimal management method. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned accounting management department, During accounting management, customize the management method based on the user's current financial situation. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned information gathering unit, It estimates the user's emotions and determines the priority of information gathering based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned information gathering unit, When gathering information, we analyze past disaster data and propose the most suitable collection method. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned information gathering unit, When collecting information, customize the collected content based on the user's current situation. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned information distribution unit, It estimates user sentiment and determines the priority of information delivery based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned information distribution unit, When distributing information, we analyze past distribution history and propose the optimal distribution method. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned information distribution unit, When distributing information, customize the content based on the user's current situation. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned group chat section is It estimates the user's emotions and prioritizes chats based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned group chat section is During group chats, the system analyzes past chat history and suggests the most suitable chat method. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned group chat section is During group chats, customize the chat content based on the user's current needs. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned bulletin board section is, It estimates user sentiment and determines the priority of message boards based on the estimated user sentiment. The system described in Appendix 2, characterized by the features described herein. (Note 26) The aforementioned bulletin board section is, When you use the bulletin board, we analyze your past usage history and suggest the most suitable way to use it. The system described in Appendix 2, characterized by the features described herein. (Note 27) The aforementioned bulletin board section is, When using the bulletin board, customize the bulletin board content based on the user's current needs. The system described in Appendix 2, characterized by the features described herein. (Note 28) The aforementioned bulletin board section is, The system estimates user sentiment and adjusts the message board notification method based on the estimated user sentiment. The system described in Appendix 2, characterized by the features described herein. (Note 29) The aforementioned bulletin board section is, When using the bulletin board, we suggest the optimal way to use it, taking into account the user's geographical location. The system described in Appendix 2, characterized by the features described herein. (Note 30) The aforementioned bulletin board section is, When using the bulletin board, the system analyzes the user's social media activity and provides relevant bulletin board content. The system described in Appendix 2, characterized by the features described herein. [Explanation of symbols]

[0189] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A date and time adjustment unit that adjusts the date and time, A reminder setting unit that notifies the date and time adjusted by the aforementioned date and time adjustment unit, The Accounting Management Department, which is responsible for accounting management, A document distribution unit that distributes accounting information managed by the aforementioned accounting management unit, The Information Gathering Department collects disaster information, The information distribution unit notifies the disaster information collected by the aforementioned information gathering unit, It includes a group chat section that provides chat tailored to specific needs. A system characterized by the following features.

2. Equipped with a bulletin board section, The aforementioned bulletin board section supports information sharing among residents. The system according to feature 1.

3. The reminder setting unit is, The date and time adjusted by the aforementioned date and time adjustment unit will be notified. The system according to feature 1.

4. The aforementioned document distribution unit, The accounting information managed by the aforementioned accounting department is distributed. The system according to feature 1.

5. The aforementioned information distribution unit, Disaster information collected by the aforementioned information gathering unit will be notified. The system according to feature 1.

6. The aforementioned group chat section is We provide chat tailored to specific needs. The system according to feature 1.

7. The aforementioned date and time adjustment unit, It estimates the user's emotions and determines the priority of scheduling based on those estimated emotions. The system according to feature 1.

8. The aforementioned date and time adjustment unit, We analyze the user's past participation history and suggest the optimal date and time. The system according to feature 1.

9. The aforementioned date and time adjustment unit, When scheduling a meeting, the system automatically detects the user's available time slots in their calendar and selects the most suitable date and time. The system according to feature 1.

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